Expertise

Topics:  Machine Learning, Artificial Intelligence, Computational Neuroscience

Tom Mitchell is interested in many areas of computer science, but especially in how to construct computers that learn from experience. At the heart of the problem of machine learning is the question of how to automatically formulate general hypotheses given a collection of very specific training examples. His research has addressed a number of approaches to this question, including statistical approaches that find regularities over large numbers of training examples, and analytical approaches that generalize from very few examples and rely instead on prior knowledge and reasoning.

Media Experience

How AI is infiltrating labor union contracts, in Pittsburgh and beyond  — Technical.ly
As unions consider how they want to cover AI in contracts, Tom Mitchell (School of Computer Science) believes white-collar, college-educated, knowledge-based professions might be the most vulnerable to disruptions from AI tools.

As artificial intelligence surges across daily life, so do concerns AI  — TribLIVE.com
Tom Mitchell, who founded Carnegie Mellon’s Department of Machine Learning in 2006, gave a simple example: You could show a computer program photos of your mother and then photos of people who are not your mother. With the gained experience, the program would be able to identify the features that distinguish who is a positive example of your mother and who is not.

Why a YouTube Chat About Chess Got Flagged for Hate Speech  — WIRED
“Fundamentally, language is still a very subtle thing,” says Tom Mitchell, a CMU professor who has previously worked with KhudaBukhsh. “These kinds of trained classifiers are not soon going to be 100 percent accurate.”